NVIDIA Dynamo-Triton

What is NVIDIA Dynamo-Triton?

NVIDIA Dynamo-Triton, formerly NVIDIA Triton Inference Server, is open-source software for serving AI models in production. One server can host models built with TensorRT, PyTorch, ONNX, OpenVINO, Python and RAPIDS FIL, and it handles real-time, batched, ensemble and audio or video streaming workloads. Dynamic batching and concurrent model execution help raise throughput, and it runs on NVIDIA GPUs, non-NVIDIA accelerators, and both x86 and ARM CPUs. It plugs into Kubernetes for scaling and Prometheus for monitoring, so it fits existing DevOps and MLOps setups. Teams that need support and stable APIs can get it as part of NVIDIA AI Enterprise, which has a 90-day evaluation license. For LLM serving at larger scale, NVIDIA points to a separate project called NVIDIA Dynamo. The server itself is free to download as source, containers or binaries.

Use cases

• Serve models from multiple frameworks on one server
• Deploy real-time inference endpoints
• Run batched inference for throughput
• Chain models with ensembles
• Scale inference on Kubernetes
• Serve audio and video streaming models
• Run inference on edge devices like Jetson

Who it's for

• ML engineers
• MLOps teams
• DevOps engineers
• AI platform teams

Why use NVIDIA Dynamo-Triton

• One server replaces separate framework-specific serving stacks, which simplifies operating mixed model fleets.
• Dynamic batching and concurrent execution help use GPUs more fully for the same traffic.
• Kubernetes and Prometheus support make it fit common cloud-native deployment and monitoring practice.
• Runs on GPUs, other accelerators and CPUs, so the same serving layer works across environments.
• Open source with an enterprise path: start from GitHub, then move to NVIDIA AI Enterprise for support.

NVIDIA Dynamo-Triton features

• Supports TensorRT, PyTorch, ONNX, OpenVINO, Python and RAPIDS FIL
• Dynamic batching and concurrent model execution
• Real-time, batched, ensemble and streaming workloads
• Model ensembles and business logic scripting
• Supports NVIDIA GPUs, other accelerators, and x86 or ARM CPUs
• Kubernetes integration for scaling
• Prometheus integration for monitoring
• Containers on NVIDIA NGC and binaries for Windows and Jetson

NVIDIA Dynamo-Triton integrations

• Kubernetes
• Prometheus
• TensorRT
• PyTorch
• ONNX
• OpenVINO
• Amazon SageMaker
• Azure Machine Learning
• NVIDIA NeMo

How to get started with NVIDIA Dynamo-Triton

1. Create a model repository that holds your models and their configuration.
2. Launch Dynamo-Triton from the NGC container, a binary release, or a build from the GitHub source.
3. Send an inference request over the server's endpoints from your application.
4. Tune dynamic batching and concurrency settings, or chain models with ensembles.
5. Scale out on Kubernetes and watch metrics in Prometheus; move to NVIDIA AI Enterprise if you need support.

NVIDIA Dynamo-Triton limitations

• Production support and stable APIs require NVIDIA AI Enterprise, which is a paid license
• LLM-specific optimizations such as disaggregated serving are in the separate NVIDIA Dynamo project
• NVIDIA AI Enterprise pricing is not listed on the page

NVIDIA Dynamo-Triton pricing

NVIDIA Dynamo-Triton is open source, so you can use and self-host it for free.

NVIDIA Dynamo-Triton plans

• Open source

  Free

  • Source code on GitHub
  • Containers from NVIDIA NGC
  • Windows and Jetson binaries

• NVIDIA AI Enterprise

  Price on vendor site

  90-day evaluation license available

  • Triton Inference Server for production inference
  • Stable APIs and support

Plans as listed on the official pricing page on 24 Oct 2026. Prices can change and may vary by country or exclude tax. Check the official pricing page.

NVIDIA Dynamo-Triton alternatives

The main NVIDIA Dynamo-Triton alternatives listed on Debuly are BentoML, vLLM, LlamaEdge, Nota AI, NVIDIA TensorRT-LLM and Replicate. The table compares pricing and platforms; order is A–Z.

1. 

   BentoML

   Open-source model serving library and Bento inference platform for deploying open and custom AI models in the cloud or your own VPC.

   Developer platformsCoding
   Free trialVisit BentoML (official site)
2. 

   LlamaEdge

   Small Rust and WebAssembly runtime that serves local LLMs through an OpenAI-compatible API on CPUs, GPUs and edge devices.

   Developer platformsCodingAutomation and agents
   Open sourceVisit LlamaEdge (official site)
3. 

   Nota AI

   Hardware-aware AI model optimization platform, NetsPresso, plus edge AI vision products for traffic, in-cabin monitoring and video analysis.

   Developer platformsAutomation and agents
   Visit Nota AI (official site)
4. 

   NVIDIA TensorRT-LLM

   Open-source Python and C++ toolkit from NVIDIA that speeds up LLM and visual generation inference on NVIDIA GPUs.

   Developer platforms
   Open sourceVisit NVIDIA TensorRT-LLM (official site)
5. 

   Replicate

   Cloud API for running, fine-tuning and deploying open-source and proprietary AI models, billed by compute time or by output.

   Developer platformsImage generationVideo
   PaidVisit Replicate (official site)
6. 

   vLLM

   Open-source engine for serving LLMs with PagedAttention, continuous batching and an OpenAI-compatible API.

   Developer platformsCoding
   Open sourceVisit vLLM (official site)

NVIDIA Dynamo-Triton FAQ

Sources & verification

Facts on this page come from the official sources below, each with the date we read it. Written by Debuly; the vendor did not review this page.

Image credits

• Logo: from developer.nvidia.com · fetched 24 Oct 2026 · Vendor trademark, used only to identify the product; removed on request
• Cover: none collected yet, so a generated tile is shown.

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